Over the past few years, a number of Graph Neural Network (GNN) architectures have been effectively employed for molecular analysis. However, generating annotated molecular data usually requires molecular dynamics or quantum chemistry calculations, which can be extremely time-consuming. To address this challenge, we introduce a predictive equivariant self-supervision technique that is founded on perturbing the 3D positions of the atoms. This method is ideal for 3D molecular data and allows the network to initially learn general structural information before fine-tuning it for specific tasks. We demonstrate that these pre-training procedures can also be utilized to fine-tune the network for learning molecular properties on a different dataset. Our pre-training method is demonstrated to surpass previously proposed solutions via extensive experiments on different standard molecular datasets.